Invited Talks and Presentations, Oral Exams, and Defenses
High-Assurance Machine Learning for Cybersecurity
Guest Lecture for CS410: Introduction to Software Engineering at University of Massachusetts Boston, April, 2023
Biologically-Aware Algorithms for Connectomics
Thesis Defense at Harvard University, April, 2021
Error Correction for Connectomics
Invited Talk at BioImage Computing @ CVPR 2019, June, 2019
Synapse-Aware Skeleton Generation for Neural Circuits
Invited Poster at Max Planck Institute--Howard Hughes Medical Institute Connectomics Conference, April, 2019
Segmentation of Electron Micrscopy Images in Connectomics
Qualifying Exam at Harvard University, May, 2018
Learning Global Features for Neuron Reconstruction in EM Images
Master's Defense at Princeton University, May, 2016
Journal and Conference Publications
SpikingVTG: A Spiking Detection Transformer for Video Temporal Grounding
Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb
Neural Information Processing Systems (NeurIPS), 2025.
Melanoma Detection with Uncertainty Quantification
SangHyuk Kim, Edward Gaibor, Brian Matejek, Daniel Haehn
IEEE International Symposium on Biomedical Imaging (ISBI), 2025.
SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection
Brian Matejek, Ashish Gehani, Nathaniel D. Bastian, Daniel J. Clouse, Bradford J. Kline, Susmit Jha
Transactions on Machine Learning Research (TMLR), 2025.
Resource-Constrained Heuristic for Max-SAT
Brian Matejek, Daniel Elenius, Cale Gentry, David Stoker, Adam Cobb
arXiv preprint arXiv:2410.09173}, 2024.
Non-Invasive Stress Monitoring from Video
Akshata Tiwari, Brian Matejek, Daniel Haehn
IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
Direct Amortized Likelihood Ratio Estimation
Adam Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha
Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.
Algorithmic Tools for Understanding the Motif Structure of Networks
Tianyi Chen, Brian Matejek, Michael Mitzenmacher, and Charalampos E. Tsourakakis
Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022.
Edge-Colored Directed Subgraph Enumeration on the Connectome
Brian Matejek, Donglai Wei, Tianyi Chen, Charalampos E. Tsourakakis, Michael Mitzenmacher, and Hanspeter Pfister
Nature Scientific Reports, 2022.
Scalable Biologically-Aware Skeleton Generation for Connectomic Volumes
Brian Matejek†, Tim Franzmeyer†, Donglai Wei, Xueying Wang, Jinglin Zhao, Kálmán Palágyi, Jeff W. Lichtman, and Hanspeter Pfister
IEEE: Transactions on Medical Imaging, 2022.
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Donglai Wei, Kisuk Lee, Hanyu Li, Ran Lu, J. Alexander Bae, Zequan Liu, Lifu Zhang, Márcia dos Santos, Zudi Lin, Thomas Uram, Xueying Wang, Ignacio Arganda-Carreras, Brian Matejek, Narayanan Kasthuri, Jeff W. Lichtman, and Hanspeter Pfister
Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021.
Two Stream Active Query Suggestion for Active Learning in Connectomics
Zudi Lin, Donglai Wei, Won-Dong Jang, Siyan Zhou, Xupeng Chen, Xueying Wang, Richard Schalek, Daniel Berger, Brian Matejek, Lee Kamentsky, Adi Peleg, Daniel Haehn, Thouis R. Jones, Toufiq Parag, Jeff Lichtman, and Hanspeter Pfister
Proceedings of European Conference on Computer Vision (ECCV), 2020.
Synapse-Aware Skeleton Generation For Neural Circuits
Brian Matejek, Donglai Wei, Xueying Wang, Jinglin Zhao, Kálmán Palágyi, and Hanspeter Pfister
Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019.
Biologically-Constrained Graphs for Global Connectomics Reconstruction
Brian Matejek, Daniel Haehn, Haidong Zhu, Donglai Wei, Toufiq Parag, and Hanspeter Pfister
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Efficient Correction for EM Connectomics with Skeletal Representation
Konstantin Dmitriev, Toufiq Parag, Brian Matejek, Arie E Kaufman, and Hanspeter Pfister
British Machine Vision Conference (BMVC), 2018.
Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field
Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Brian Matejek, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, and Daniel Keim
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2018.
Compresso: Efficient Compression of Segmentation Data for Connectomics
Brian Matejek, Daniel Haehn, Fritz Lekschas, Michael Mitzenmacher, and Hanspeter Pfister
Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2017.
Scalable Interactive Visualization for Connectomics
Daniel Haehn, John Hoffer, Brian Matejek, Adi Suissa-Peleg, Ali K Al-Awami, Lee Kamentsky, Felix Gonda, Eagon Meng, William Zhang, Richard Schalek, Alyssa Wilson, Toufiq Parag, Johanna Beyer, Verena Kaynig, Thouis R. Jones, James Tompkin, Markus Hadwiger, Jeff W. Lichtman, and Hanspeter Pfister
MDPI Informatics, 2017.
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis, Srinivas C. Turaga, Xuewen Zhang, Brian Matejek, Lee Kamentsky, Jeff W. Lichtman, and Hanspeter Pfister
arXiv preprint arXiv:1707.08935, 2017.
Learning Hierarchical Semantic Segmentations of LIDAR Data
David Dohan, Brian Matejek, and Thomas Funkhouser
IEEE International Conference of 3D Vision, 2015.
Workshop Papers
A Preliminary Study into the Conceptual Design of Aircraft using Simulation‑Based Inference
Aurelien Ghiglino, Daniel Elenius, Anirban Roy, Ramneet Kaur, Manoj Acharya, Colin Samplawski, Brian Matejek, Susmit Jha, Juan Alonso, Adam D. Cobb
Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems (NeurIPS), 2025.
Breaking Bad: Interpretability‑Based Safety Audits of State‑of‑the‑Art LLMs
Krishiv Agarwal, Ramneet Kaur, Colin Samplawski, Manoj Acharya, Anirban Roy, Daniel Elenius, Brian Matejek, Adam D. Cobb, Susmit Jha
Lock‑LLM Workshop: Prevent Unauthorized Knowledge Use from Large Language Models at Neural Information Processing Systems (NeurIPS), 2025.
Enhancing Semantic Clustering for Uncertainty Quantification & Conformal Prediction by LLMs
Ramneet Kaur, Colin Samplawski, Adam D. Cobb, Anirban Roy, Brian Matejek, Manoj Acharya, Daniel Elenius, Alexander Michael Berenbeim, John A. Pavlik, Nathaniel D. Bastian, Susmit Jha
Statistical Foundations of LLMs and Foundation Models at Neural Information Processing Systems (NeurIPS), 2024.
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
Ramneet Kaur, Colin Samplawski, Adam D. Cobb, Anirban Roy, Brian Matejek, Manoj Acharya, Daniel Elenius, Alexander Michael Berenbeim, John A. Pavlik, Nathaniel D. Bastian, Susmit Jha
Safe Generative AI at Neural Information Processing Systems (NeurIPS), 2024.
SpikingVTG: Saliency Feedback Gating Enabled Spiking Video Temporal Grounding
Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb
Adaptive Foundation Models Workshop at Neural Information Processing Systems (NeurIPS), 2024.
SpikingVTG: Saliency Feedback Gating Enabled Spiking Video Temporal Grounding
Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb
Machine Learning and Compression Workshop at Neural Information Processing Systems (NeurIPS), 2024.
Non‑Invasive Stress Monitoring from Video
Akshata Tiwari, Brian Matejek, Daniel Haehn
Women in Machine Learning Workshop at Neural Information Processing Systems (NeurIPS), 2024.
Safeguarding Network Intrusion Detection Models from Zero‑day Attacks and Concept Drift
Brian Matejek, Ashish Gehani, Nathaniel D. Bastian, Daniel J. Clouse, Bradford J. Kline, Susmit Jha
Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI Conference on Artificial Intelligence (AAAI), 2024.
Direct Amortized Likelihood Ratio Estimation
Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, and Susmit Jha
Machine Learning and the Physical Sciences Workshop at Neural Information Processing Systems (NeurIPS), 2023.
Independent Research, Written Exams, and Theses
Biologically-Aware Algorithms for Connectomics
Ph.D. Dissertation at Harvard University, April, 2021
Segmentation of Electron Microscopy Images for Connectomics
Qualifying Exam at Harvard University, May, 2018
Learning Global Features for Neuron Reconstruction in EM Images
Master's Thesis at Princeton University, May, 2016
Detecting Objects Using Google Street View Data
Independent Research at Princeton University, December, 2013
A Computational Analysis of Arbitrage Opportunities in Sports Gambling
Independent Research at Princeton University, May, 2013